In-Surely

Interactive insurance-policy QA system built with retrieval-augmented generation.

Why I Built This

Insurance policy documents are dense, repetitive, and difficult to navigate when someone needs a clear answer quickly. I built In-Surely to reduce that friction with retrieval-grounded Q and A over real policy text and tables. The project was motivated by a practical question: can we make policy understanding less intimidating without sacrificing precision.

What It Does

  • Extracts text and tabular content from policy PDFs.
  • Builds semantic embeddings for retrieval.
  • Uses cache-aware query handling for faster repeated questions.
  • Applies cross-encoder reranking to improve final context quality.
  • Generates final responses with GPT-based synthesis.

Outcome

A full notebook-based RAG workflow that can be run in Colab and adapted to other document-heavy domains.